Princeton and Meta AI researchers present 'Lory': a fully differentiable MoE model designed for autoregressive language model pre-training

Merge-of-experts (MoE) architectures use sparse activation to initialize scaling of model sizes while preserving high inference and training efficiency. However, training the router network creates the challenge of optimizing a discrete and non-differentiable objective despite efficient scaling of the MoE models. Recently, a MoE architecture called SMEAR was introduced, which is completely undifferentiable and smoothly merges experts into the parameter space. SMEAR is very efficient, but its effectiveness is limited to small-scale tuning experiments on downstream classification tasks.

Sparsely activated MoE models have emerged as a useful method to scale model sizes efficiently. The sparse MoE architecture adapts to transformative models to achieve better performance in machine translation. Traditional MoE models are trained to route input data to expert modules, resulting in a discrete and non-differentiable decision learning problem. Additionally, top 1 or top 2 routing strategies are used to train these existing models based on a designed load balancing objective. The Ministry of Education models are complicated when trained, which creates the problem of training instability, subspecialization of experts, and inefficient training.

Researchers from Princeton University and Meta ai present Lori, a method for scaling MoE architectures to pre-training autoregressive language models. Lory consists of two main techniques: (a) a casual segment routing strategy that is efficient in expert fusion operations while maintaining the autoregressive nature of language models (LMs), and (b) a batch processing method of Similarity-based data supports expert specialization by creating groups for similar documents during training. Furthermore, Lory models outperform state-of-the-art MoE models with the help of token-level routing instead of segment-level routing.

Casual segment routing, the first technique, is divided into smaller segments with a fixed length for a sequence of input tokens. The original segment is used to obtain the weight of the router and evaluate the merged expert for the next segment. Segment-level routing performed by cues during inference can lead to insufficient specialization of experts because the text data for pre-training language models typically combine random sets of documents. Then, the second technique, i.e., similarity-based data batching for MoE training, overcomes this challenge by grouping similar documents together to create sequential segments. This technique is used to train LM, resulting in efficient training for expert routing.

Lory shows outstanding results for several factors. They are:

• Training efficiency and convergence: Lory achieves an equivalent loss level with less than half the training tokens for models 0.3B and 1.5B, indicating better performance with the same training calculation.
• Language modeling: The proposed MoE models outperform the dense baseline in all domains, leading to a decrease in perplexity. For example, compared to the dense 0.3B model, the 0.3B/32E models achieve a relative improvement of 13.9% on Books.
• Subsequent tasks: The 0.3B/32E model achieves an average performance increase of +3.7% in common sense reasoning, +3.3% in reading comprehension, +1.5% in reading comprehension, and +11.1% in classifying. texts.

In conclusion, researchers from Princeton University and Meta ai proposed Lory, a fully differentiable MoE model designed for autoregressive language model pre-training. Lory consists of two main techniques: a casual segment routing strategy and a similarity-based data batching method. The proposed method outperforms its dense counterpart in language modeling and downstream tasks, and the trained experts are highly specialized and capable of capturing domain-level information. Future work includes extending Lory and integrating routing at the token and segment level by developing efficient decoding methods for Lory.

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